Technology for non-invasive observation of soft tissues of the body has provided significant advances in the field of medicine. For example, a number of techniques now make it possible to routinely image anatomical structures such as the heart, colon, bronchus, and esophagus within the body.
The widespread availability of skilled technicians and reduction in cost of the necessary equipment has encouraged the use of non-invasive imaging as a part of routine preventive care. Non-invasive imaging reduces the risk of observation-related injury or complication and reduces discomfort and inconvenience for the observed patient. These advantages encourage patients to undergo more frequent screening and permits earlier detection of potentially life-threatening conditions. For example, malignant or premalignant conditions can be identified and diagnosed at an early stage, when treatment is more likely to be successful.
In one commonly used imaging technique called Computed Tomography Imaging (“CT Scan”), multiple two-dimensional radiographic image cross-sections are taken of a particular region of the patient's body. A physician can then analyze the sectioned images to detect any anomalies within the observed section and judge which anomalies are of interest, requiring further attention or treatment.
To assure adequate coverage of the section being observed, a large number of cross-sectional slices can be obtained to increase the observation resolution. However, as the number of slices increases, the amount of data presented to the physician becomes more difficult to efficiently analyze. Accordingly, various software techniques have been applied with some success to aid in analyzing the data to identify anomalies.
Although progress has been made in employing software to assist in detection of anatomical anomalies, there are significant limitations to the current automated techniques. For example, one problem consistently plaguing such systems is the overabundance of false positives.
Typically, the software approach correctly identifies anomalies of interest (i.e., the software exhibits superior sensitivity). However, the software also tends to incorrectly identify too many structures as anomalies of interest (i.e., the software exhibits poor specificity). A feature incorrectly identified as an anomaly of interest is called a “false positive.”
False positives are troublesome because any identified positives must be considered and evaluated by a human classifier (such as the physician or a technician). Even if an anomaly can be quickly dismissed as a false positive, too many false positives consume an inordinate amount of time and limit the usefulness of the software-based approach.
A particular example of a common problem with automated CT analysis is the misidentification of the ileocecal valve (QCV) as a polyp during CT colonography. Because the ileocecal valve exhibits the characteristics of a polyp (for example, it has the morphology of a large polyp), it is likely to be detected by software as a polyp and presented as an anomaly of interest.
There thus remains a need for a way to improve the computer-based approaches for identifying anomalies of interest in anatomical structures. For example, specificity can be improved by developing techniques which identify potential pathologies while reducing misclassification of normal structures as features of interest.